Distributed Generation Market Demand Model (dGen): Documentation

Sigrin, Ben; Gleason, Michael; Preus, Robert; Baring-Gould, Ian; Margolis, Robert

doi:10.2172/1239054

Cited by 77 publications

(113 citation statements)

References 25 publications

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“…Technologies include both conventional (coal, nuclear, gas and oil) and renewable (wind, solar, bio-power, geothermal, hydro) energy production. While the ReEDS model optimizes the technology investments from a system perspective, it also incorporates decisions for the distributed energy resources, such as rooftop solar technologies, through its integration with dGen models [33]. The dGen (aka dSolar) model estimates the yearly anticipated construction of rooftop-solar panel based on customer cash-flow analysis and market-adoption theory, subject to the variability in the market signals and housing capability [8].…”

Section: Regional Energy Deployment Systemmentioning

confidence: 99%

Enabling immersive engagement in energy system models with deep learning

Bugbee

Bush

Gruchalla

et al. 2019

Statistical Analysis

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Complex ensembles of energy simulation models have become significant components of renewable energy research in recent years. Often the significant computational cost, high‐dimensional structure, and other complexities hinder researchers from fully utilizing these data sources for knowledge building. Researchers at National Renewable Energy Laboratory have developed an immersive visualization workflow to dramatically improve user engagement and analysis capability through a combination of low‐dimensional structure analysis, deep learning, and custom visualization methods. We present case studies for two energy simulation platforms.

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Section: Regional Energy Deployment Systemmentioning

confidence: 99%

Enabling immersive engagement in energy system models with deep learning

Bugbee

Bush

Gruchalla

et al. 2019

Statistical Analysis

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“…PV generation capacity in the USA is given as a function of the PV system cost, represented here by the LCOE for an unsubsidized single‐axis tracking utility‐scale PV (UPV) system in a location with average US solar resource (LCOE in 2015 US dollars is used here as a summary metric; however, it is not used by the model to project adoption). Distributed rooftop PV (DPV) is included in the calculation using NREL's distributed generation adoption model (dGen), with DPV cost metrics for each scenario selected to be compatible with the UPV cost for that scenario. PV system cost was assumed to ramp down over time, reaching the value indicated in Table in 2030, then declining by an additional 15% through 2040.…”

Section: Deployment Modelmentioning

confidence: 99%

Comparing supply and demand models for future photovoltaic power generation in the USA

Basore

Cole

2018

Progress in Photovoltaics

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We explore the plausible range of future deployment of photovoltaic generation capacity in the USA using a supply-focused model based on supply-chain growth constraints and a demandfocused model based on minimizing the overall cost of the electricity system. Both approaches require assumptions based on previous experience and anticipated trends. For each of the models, we assign plausible ranges for the key assumptions and then compare the resulting PV deployment over time. Each model was applied to 2 different future scenarios: one in which PV market penetration is ultimately constrained by the uncontrolled variability of solar power and one in which low-cost energy storage or some equivalent measure largely alleviates this constraint. The supply-focused and demand-focused models are in substantial agreement, not just in the long term, where deployment is largely determined by the assumed market penetration constraints, but also in the interim years. For the future scenario without low-cost energy storage or equivalent measures, the 2 models give an average plausible range of PV generation capacity in the USA of 150 to 530 GW dc in 2030 and 260 to 810 GW dc in 2040. With low-cost energy storage or equivalent measures, the corresponding ranges are 160 to 630 GW dc in 2030 and 280 to 1200 GW dc in 2040. The latter range is enough to supply 10% to 40% of US electricity demand in 2040, based on current demand growth.

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“…Similarly, Rai and Robinson [16] calibrated agents using geographic data sets and customer surveys in Austin, Texas, using the theory of planned behavior [26] to simulate DPV adoption choices. NREL's dGen model [27], and its predecessor SolarDS [19], take a hybrid Bass Model and ABM approach wherein synthetic spatially-aware agents are instantiated with characteristics reflective of their surroundings, and their adoption patterns are fitted to a Bass Model logistic curve based on prevailing economic trends.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Resolution Implications for Agent-based Distributed Energy Resource Customer Adoption Models

Kwasnik

Sigrin

Bielen

2019

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Distributed photovoltaics (DPV) are a growing source of electricity generation in the United States, and with adoption driven by customer behavior and localized economics, projecting the deployment of this technology is a challenging analytical problem. Moreover, understanding the sources of uncertainty in customer adoption models and how they can be reduced is important to a range of stakeholders that use their outputs, including grid planners, regulators, and industry. Most prior studies have used top-down methods, such as the use of population central tendencies to project aggregate adoption. In contrast, a growing field of work seeks to use bottom-up methods (i.e., individual-level decision-making).We explore trade-offs of top-down and bottom-up methods in their precision and computational burden using the National Renewable Energy Laboratory's (NREL's) Distributed Generation Market Demand (dGen) model, an agent-based model of residential and nonresidential distributed PV adoption. In particular, we assess the role of agent resolution in instantiating statistically-representative populations in the model-and the resulting variance of model projections at the state, sector, and county levels. At low sampling rates, the model resembles a top-down model, whereas as sampling rates increase dGen converges to a bottom-up structure by simulating more unique customer types. Though sampling-based models such as dGen can be operated with many agents to ensure accuracy, doing so greatly increases the computational burden of the simulation. This report lends insight into whether high-resolution results can be approximated sufficiently well using fewer computational resources.vii

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Distributed Generation Market Demand Model (dGen): Documentation

Cited by 77 publications

References 25 publications

Enabling immersive engagement in energy system models with deep learning

Enabling immersive engagement in energy system models with deep learning

Comparing supply and demand models for future photovoltaic power generation in the USA

Quantifying Resolution Implications for Agent-based Distributed Energy Resource Customer Adoption Models

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